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+\begin{thebibliography}{10}
+
+\bibitem{abadi2016deep}
+Martin Abadi, Andy Chu, Ian Goodfellow, H~Brendan McMahan, Ilya Mironov, Kunal
+  Talwar, and Li~Zhang.
+\newblock Deep learning with differential privacy.
+\newblock In {\em Proceedings of the 2016 ACM SIGSAC conference on computer and
+  communications security}, pages 308--318, 2016.
+
+\bibitem{bellovin2019privacy}
+Steven~M Bellovin, Preetam~K Dutta, and Nathan Reitinger.
+\newblock Privacy and synthetic datasets.
+\newblock {\em Stan. Tech. L. Rev.}, 22:1, 2019.
+
+\bibitem{ding2021retiring}
+Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt.
+\newblock Retiring adult: New datasets for fair machine learning.
+\newblock {\em Advances in Neural Information Processing Systems}, 34, 2021.
+
+\bibitem{gan}
+Ian~J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David
+  Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.
+\newblock Generative adversarial nets.
+\newblock In {\em Proceedings of the 27th International Conference on Neural
+  Information Processing Systems - Volume 2}, NIPS'14, page 2672–2680,
+  Cambridge, MA, USA, 2014. MIT Press.
+
+\bibitem{EO}
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+\newblock Equality of opportunity in supervised learning.
+\newblock {\em CoRR}, abs/1610.02413, 2016.
+
+\bibitem{hawkins2004problem}
+Douglas~M Hawkins.
+\newblock The problem of overfitting.
+\newblock {\em Journal of chemical information and computer sciences},
+  44(1):1--12, 2004.
+
+\bibitem{jordon2021hide}
+James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul Elbers,
+  Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, and Mihaela
+  van~der Schaar.
+\newblock Hide-and-seek privacy challenge: Synthetic data generation vs.
+  patient re-identification.
+\newblock In {\em NeurIPS 2020 Competition and Demonstration Track}, pages
+  206--215. PMLR, 2021.
+
+\bibitem{cgan}
+Mehdi Mirza and Simon Osindero.
+\newblock Conditional generative adversarial nets, 2014.
+
+\bibitem{dcgan}
+Alec Radford, Luke Metz, and Soumith Chintala.
+\newblock Unsupervised representation learning with deep convolutional
+  generative adversarial networks, 2016.
+
+\bibitem{cnn}
+Waseem Rawat and Zenghui Wang.
+\newblock Deep convolutional neural networks for image classification: A
+  comprehensive review.
+\newblock {\em Neural Computation}, 29(9):2352--2449, 2017.
+
+\bibitem{shokri2017membership}
+Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.
+\newblock Membership inference attacks against machine learning models.
+\newblock In {\em 2017 IEEE symposium on security and privacy (SP)}, pages
+  3--18. IEEE, 2017.
+
+\bibitem{vgg16}
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+\newblock Very deep convolutional networks for large-scale image recognition,
+  2015.
+
+\bibitem{song2020overlearning}
+Congzheng Song and Vitaly Shmatikov.
+\newblock Overlearning reveals sensitive attributes, 2020.
+
+\bibitem{stadler2020synthetic}
+Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso.
+\newblock Synthetic data-a privacy mirage.
+\newblock {\em arXiv preprint arXiv:2011.07018}, 2020.
+
+\bibitem{ctgan}
+Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni.
+\newblock Modeling tabular data using conditional gan, 2019.
+
+\bibitem{yeom}
+Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha.
+\newblock Privacy risk in machine learning: Analyzing the connection to
+  overfitting, 2018.
+
+\bibitem{zhifei2017cvpr}
+Zhifei Zhang, Yang Song, and Hairong Qi.
+\newblock Age progression/regression by conditional adversarial autoencoder.
+\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
+  (CVPR)}. IEEE, 2017.
+
+\end{thebibliography}